Advances in computerized and powered artificial legs show great promise to permit persons with lower limb amputations to perform versatile activities beyond level ground walking. These prostheses are, however, inadequate for users to perform seamless transitions between activities due to the lack of neural control. To "tell" the prosthesis the intended movement, the user must make extra body motions or use a remote key fob, which are both cumbersome and not robust. Obtaining decisions directly from the user through a neural control interface is crucial to providing accurate, intuitive control of computerized artificial legs. Our long-term goal is to develop a neural-controlled artificial knee and/or ankle to improve the function of computerized artificial legs and the quality of life of people with lower limb amputations. Towards this goal, we propose to develop a robust neural-machine interface that can recognize the user's intended lower limb tasks in real-time. A functional, embedded neural interfacing system will be delivered at the end of this project that may start a complete paradigm shift in the design of computerized artificial legs. The specific aims of this grant are: Aim 1: Develop a neural interface algorithm that accurately and responsively decodes the user's intended lower limb tasks and task transitions. Aim 2: Implement the algorithm designed in Aim 1 on real-time embedded hardware. Aim 3: Evaluate the real-time neural interfacing system on subjects with knee disarticulation or transfemoral amputations. We propose a neural-mechanical-fusion-based interfacing design for the development of the algorithm (Aim 1). The algorithm will integrate the neuromuscular control information gathered through electromyographic (EMG) recordings with mechanical feedback from the prosthesis to achieve improved accuracy for identifying user intent. A phase-dependent pattern recognition strategy is proposed to ensure a fast system time response for real-time application. Additional components such as sensor fault detectors and a finite-state machine will be designed to enhance the system robustness. The designed algorithm will be implemented on real-time testing hardware composed of a self-constrained instrumented leg and an embedded system (Aim 2). The data structures and programs will be optimized to make the best use of the embedded architecture and the multilevel memory hierarchy for real-time operation. The finalized real-time neural- machine interface will be evaluated on patients with knee disarticulation or transfemoral amputations, which are high and challenging levels (Aim 3). PUBLIC HEALTH RELEVANCE: The neural-machine interface developed for neural control of artificial legs will lead to improved functional usage of impaired limbs, reduced disability, and improved quality of life of patients with lower limb amputations.